Why Training Must Align with Organizational Goals

Organizational Imperatives

Thepressures on organizations aren’t lessening; the pressure to be more agile and adapt continue to increase. And there are many calls for organizations to do things in new ways. There are similarities between the models proposed for these new ways of working, and this isn’t wholly surprising. New research on how humans perform best is yielding results that explain these new models. And the implications for business are important.

The directions point away from industrial age models. The old days, when a few fortunate made the decisions for the many who were working as cogs in a machine, have passed. The time for ‘time and motion’ studies is gone. The rote tasks, things that can be automated are being automated. What’s left is the important work, the knowledge work, work that humans do best.

Knowledge work is critical in this new information age. The increasing need is for abilities to problem-solve, research, design, trouble-shoot, and more. This work, when the answer isn’t known, can (and should) also be considered learning. And we have many good insights as to what leads to the best outcomes for such activities. The question is whether we in Learning & Development (L&D) are practicing these new methods, and supporting them in the organization.

The evidence is that we’re not. Reports continue to indicate that organizations aren’t learning fast enough, that they’re not leveraging the opportunities, and employees are not engaged. We need a different approach. In short, our organizations are not aligned with what we now know about how we think, work, and learn. There is a role for L&D in this important work, but we have to do so in enlightened ways. We can do better, but to do so we need to know where the opportunities are.

How we learn

Let’s start with a relatively familiar area: learning. The majority of L&D approaches to formal learning are, frankly, not matched with what we know about what leads to successful learning. We are focused on efficiencies, measuring things like cost/seat/hour, instead of impact. And we’re not applying what’s known from research into the learning sciences.

For one, we know learning is best accomplished by doing, where we apply knowledge to solve problems like we will use that knowledge after the learning experience. However, too much of our instruction is focused on content, and we tend to evaluate the learning experience with knowledge checks instead of application. While knowledge may be necessary, it is not sufficient. And while designing more meaningful practice may take a wee bit longer, it is the only thing that is going to reliably lead to new abilities.

And most of our learning is based upon an ‘event’ model, where we have people for a fixed block of time. This again is efficient, but not effective. Research shows that people need to revisit learning in bits over time, and that ‘massed’ practice (e.g. a large amount of practice in a short period of time) doesn’t stick. Yet where are our extended learning models? Similarly, practice needs to be varied, mixed in with other sorts of practice, yet we tend to focus on one topic at a time.

And we largely ignore the emotional side of learning, despite evidence that it matters. We may use ‘extrinsic’ motivation, where we wrap some gratuitous theme around our knowledge quizzes, but we really don’t try to help people understand enough about why they should care. We also seldom address learners’ anxieties about learning, nor focus explicitly on building confidence.

At core, a learning curriculum should be a series of activities, which have learners perform and create, as opposed to a suite of content. The content is an adjunct to support the activity, not the other way around. We developed apprenticeships as a natural way to learn, and we need to return to a ‘cognitive apprenticeship’ model to more closely align our instructional approaches with how our brains really learn.

How we think

Our understanding of how we think has also changed. The old model was that we are good at formal reasoning, and do all the important thinking in our heads. Consequently, we default to courses to put everything in our heads. And the evidence suggests that such a model is fundamentally wrong.

The view that all our thinking is done in our heads doesn’t concur with the evidence. We use powerful cognitive adjuncts, representational tools like spreadsheets and maps and more to accomplish our goals. Our problem-solving system is fundamentally distributed around the world across our representations as well as in our heads. Yet we don’t take enough advantage of our tools.

Further, there’s considerable evidence now that our reasoning is flawed in predictable ways. We are prone to a number of problems in thinking that lead us astray. One of the results is that we tend to trust our instincts too often. We are justified in areas we have expertise, but we typically extend those judgments too far. Also, formal thinking is effortful, so we use it to justify our choices rather than to make the choices!

There are a host of phenomena that are artifacts of our cognitive architecture. We are prone to search for evidence that confirms our suspicions, rather than evidence that proves us wrong. We tend to solve new problems in the ways that solved old problems, even if there’s a better way. And we tend to be limited in how we use tools instead of exploring the full suite of possibilities. Finally, we have some randomness built in that means we’re unlikely to be able to reliable repeat rote actions, nor recall accurately arbitrary information.

We have evolved approaches that compensate for these faults, but as designers we tend to forget about them, using courses as our only solution. Yet there are many times when courses don’t make sense! If there’re large amounts of arbitrary information, it’s better to put the information in the world to be looked up as needed. Also if the information is changing fast enough that it would be regularly required to be relearned (old information interferes with new), we should be not trying to have people remember it. Save rote learning for those (few) times when it absolutely, positively, has to be in the head, and then drill it and support it.

Our design processes need to change to include looking for other solutions than courses. So, for instance, rather than trust our abilities to follow procedures accurately, we can use checklists and flowcharts to help us remember. Courses should be our last resort, because doing learning right means that we have to do better than just knowledge dump and information test. There are many ways in which we can use cognitive tools to help improve outcomes, but we don’t see enough application of this coming out of the L&D group.

How we work

One other fundamental myth about our ability to solve problems is that innovation is individual, yet research show that the best outputs come from individuals collaborating rather than one person working on their own. This is what has led to the proposals for new organizational structures that are ‘podular’. Why is this?

The reality is that when people work together, the outcome is superior to the output of an individual. The saying goes that ‘the room is smarter than the smartest person in the room’, but there’s a caveat: you need to use good processes. When people work well together, however, the output is better.

Don’t assume people know how to work and play well together. There are skills involved in asking for help in ways that people will offer, and likewise offering to help in ways that people will accept. Similarly, there are ways to run brainstorming that lead to better outcomes, and meetings likewise.

Also important is the culture of the organization. If it’s not safe to share, people won’t. If people aren’t held accountable, no one will contribute. If your organization isn’t open to new ideas, things can’t change. People need time for reflection for new ideas to emerge. If you don’t tap into diversity, you’re limiting the pool of potential insights. And ultimately there has to be a commitment to experimentation and a tolerance for mistakes.

There’s a role for L&D here too, helping share and develop the necessary processes, facilitating the work, incorporating the tools, and building the culture. Helping individuals learn to be good learners improves the overall organization. This is, arguably, the best investment organizations can make in their long term

Aligning

There are ways to get better on learning, on supporting performance, and on innovating. Technology is a tool that can support this, but it is not an answer in itself. What’s needed is to start with an understanding of the gaps. From there, you can sort through the improvement proposals and choose ones that mesh with your situation. Ultimately, you have to start making changes. To start, I recommend making changes in how things operate in your own unit. From there, you can look to understand the requirements and move forward.

There’s a real opportunity, even an imperative, to change how we align with the organization and the employees. And the opportunity is for L&D to be the catalyst for positive change. Not surprisingly, it’s about understanding where you want to go, and making small steps in that direction. The possibilities are on the table, are you ready to take the first step?

About Clark Quinn

Clark Quinn, Ph.D., helps Fortune 500, government, not-for-profit, and educational organizations align technology with how we think, work, and learn. He integrates creativity, cognitive science, and technology to develop award-winning online content, educational computer games, and websites, as well as adaptive, mobile, and performance support systems. After an academic career, Dr. Quinn has served as an executive in online and elearning initiatives and has an international reputation as a consultant, speaker, and author, with five books and numerous articles and chapters. Clark works through Quinnovation.com, and was awarded the eLearning Guild’s Guild Master award in 2012.